


"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)
def      UpperCAmelCase     (       _lowercase :   Optional[Any]			,   _lowercase :   Optional[Any]=False	)						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			[]
	for i in range(config.num_hidden_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")	)
		rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")	)
		rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")	)
		rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")	)
		rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")	)
		rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")	)
		rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")	)
		rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")	)
		rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")	)
		rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")	)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ('''cls_token''', '''deit.embeddings.cls_token'''),
	        ('''dist_token''', '''deit.embeddings.distillation_token'''),
	        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
	        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
	        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
	    ]	)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ('''norm.weight''', '''layernorm.weight'''),
		        ('''norm.bias''', '''layernorm.bias'''),
		        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
		        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
		    ]	)

		# if just the base model, we should remove "deit" from all keys that start with "deit"
		lowerCAmelCase_      							=			[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''	) else pair for pair in rename_keys]
	else:
		# layernorm + classification heads
		rename_keys.extend(
		    [
		        ('''norm.weight''', '''deit.layernorm.weight'''),
		        ('''norm.bias''', '''deit.layernorm.bias'''),
		        ('''head.weight''', '''cls_classifier.weight'''),
		        ('''head.bias''', '''cls_classifier.bias'''),
		        ('''head_dist.weight''', '''distillation_classifier.weight'''),
		        ('''head_dist.bias''', '''distillation_classifier.bias'''),
		    ]	)

	return rename_keys
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   Union[str, Any]=False	)						-> Union[str, Any]:


	"""simple docstring"""




	for i in range(config.num_hidden_layers	):
		if base_model:
			lowerCAmelCase_      							=			''''''
		else:
			lowerCAmelCase_      							=			'''deit.'''
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		lowerCAmelCase_      							=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""	)
		lowerCAmelCase_      							=			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""	)
		# next, add query, keys and values (in that order) to the state dict
		lowerCAmelCase_      							=			in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCAmelCase_      							=			in_proj_bias[: config.hidden_size]
		lowerCAmelCase_      							=			in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCAmelCase_      							=			in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowerCAmelCase_      							=			in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCAmelCase_      							=			in_proj_bias[-config.hidden_size :]
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   List[Any]			,   _lowercase :   int	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			dct.pop(_lowercase	)
	lowerCAmelCase_      							=			val
def      UpperCAmelCase     (       )						-> int:


	"""simple docstring"""




	lowerCAmelCase_      							=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase_      							=			Image.open(requests.get(_lowercase			,   stream=_lowercase	).raw	)
	return im
@torch.no_grad()
def      UpperCAmelCase     (       _lowercase :   Tuple			,   _lowercase :   Optional[int]	)						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			DeiTConfig()
	# all deit models have fine-tuned heads
	lowerCAmelCase_      							=			False
	# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
	lowerCAmelCase_      							=			1_0_0_0
	lowerCAmelCase_      							=			'''huggingface/label-files'''
	lowerCAmelCase_      							=			'''imagenet-1k-id2label.json'''
	lowerCAmelCase_      							=			json.load(open(hf_hub_download(_lowercase			,   _lowercase			,   repo_type='''dataset'''	)			,   '''r'''	)	)
	lowerCAmelCase_      							=			{int(_lowercase	): v for k, v in idalabel.items()}
	lowerCAmelCase_      							=			idalabel
	lowerCAmelCase_      							=			{v: k for k, v in idalabel.items()}
	lowerCAmelCase_      							=			int(deit_name[-6:-4]	)
	lowerCAmelCase_      							=			int(deit_name[-3:]	)
	# size of the architecture
	if deit_name[9:].startswith('''tiny'''	):
		lowerCAmelCase_      							=			1_9_2
		lowerCAmelCase_      							=			7_6_8
		lowerCAmelCase_      							=			1_2
		lowerCAmelCase_      							=			3
	elif deit_name[9:].startswith('''small'''	):
		lowerCAmelCase_      							=			3_8_4
		lowerCAmelCase_      							=			1_5_3_6
		lowerCAmelCase_      							=			1_2
		lowerCAmelCase_      							=			6
	if deit_name[9:].startswith('''base'''	):
		pass
	elif deit_name[4:].startswith('''large'''	):
		lowerCAmelCase_      							=			1_0_2_4
		lowerCAmelCase_      							=			4_0_9_6
		lowerCAmelCase_      							=			2_4
		lowerCAmelCase_      							=			1_6

	# load original model from timm
	lowerCAmelCase_      							=			timm.create_model(_lowercase			,   pretrained=_lowercase	)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	lowerCAmelCase_      							=			timm_model.state_dict()
	lowerCAmelCase_      							=			create_rename_keys(_lowercase			,   _lowercase	)
	for src, dest in rename_keys:
		rename_key(_lowercase			,   _lowercase			,   _lowercase	)
	read_in_q_k_v(_lowercase			,   _lowercase			,   _lowercase	)

	# load HuggingFace model
	lowerCAmelCase_      							=			DeiTForImageClassificationWithTeacher(_lowercase	).eval()
	model.load_state_dict(_lowercase	)

	# Check outputs on an image, prepared by DeiTImageProcessor
	lowerCAmelCase_      							=			int(
	    (2_5_6 / 2_2_4) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
	lowerCAmelCase_      							=			DeiTImageProcessor(size=_lowercase			,   crop_size=config.image_size	)
	lowerCAmelCase_      							=			image_processor(images=prepare_img()			,   return_tensors='''pt'''	)
	lowerCAmelCase_      							=			encoding['''pixel_values''']
	lowerCAmelCase_      							=			model(_lowercase	)

	lowerCAmelCase_      							=			timm_model(_lowercase	)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(_lowercase			,   outputs.logits			,   atol=1E-3	)

	Path(_lowercase	).mkdir(exist_ok=_lowercase	)
	print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)
	print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
	image_processor.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--deit_name',
		    default='vit_deit_base_distilled_patch16_224',
		    type=str,
		    help='Name of the DeiT timm model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		lowercase_							=							parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



"""simple docstring"""



from collections import Counter
from timeit import timeit
def      UpperCAmelCase     (       _lowercase :   str = ""			,   )						-> bool:


	"""simple docstring"""




	return sum(c % 2 for c in Counter(input_str.replace(''' '''			,   ''''''	).lower()	).values()	) < 2
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> bool:


	"""simple docstring"""




	if len(_lowercase	) == 0:
		return True
	lowerCAmelCase_      							=			input_str.replace(''' '''			,   ''''''	).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	lowerCAmelCase_      							=			{}

	for character in lower_case_input_str:
		lowerCAmelCase_      							=			character_freq_dict.get(_lowercase			,   0	) + 1
	lowerCAmelCase_      							=			0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> None:


	"""simple docstring"""




	print('''\nFor string = '''			,   _lowercase			,   ''':'''	)
	print(
	    '''> can_string_be_rearranged_as_palindrome_counter()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome_counter(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )
	print(
	    '''> can_string_be_rearranged_as_palindrome()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )


if __name__ == "__main__":
		lowercase_							=							input(
		    'Enter string to determine if it can be rearranged as a palindrome or not: '
		).strip()
		benchmark(check_str)
		lowercase_							=							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")